Applying Machine Learning to Maximize Agricultural Yield to Handle the Food Crisis and Sustainable Growth

Abstract

The intelligent agriculture system is a farming-based project, and it will suggest the best crops in the region and maximum yield. Thus, it will affect all the stakeholders related to farming. It may use various technologies such as big data and ML (machine learning). These technologies will help us in fetching the data to train it according to the needs. The agricultural sector also has a significant impact on the country's GDP (gross domestic product). India is rich in the area of agriculture, but the yields per hectare are exceptionally low as compared to the land. The business logic in Python uses machine learning techniques to predict the most suitable crops in the forecasted weather and soil conditions at a specified location. The proposed system will integrate the data obtained from the weather department and by applying machine learning algorithms: Naïve Bayes (polynomial) and support vector machine (SVM) and unsupervised machine learning algorithms like k-means clustering multiple linear regression for weather and environmental conditions are made.